Patterns of failure and histopathologic outcome predictors following definitive radiotherapy and planned neck dissection with residual disease
Bibliographic record
Abstract
BACKGROUND: Our aim was to report patterns of failure and histopathological predictors in patients with head and neck cancer treated by planned neck dissection. METHODS: We reviewed all new patients with head and neck cancer who underwent a planned neck dissection in our institution from 1998 to 2007. Patterns of failure after positive planned neck dissection were reported. The frequency and predictive value of histopathologic features were analyzed. RESULTS: Fifty positive planned neck dissection and 144 negative planned neck dissection cases were identified. The positive planned neck dissection cohort had lower 5-year overall survival (OS; 33% vs 77%; p < .01), a significantly higher distant metastasis (DM; 44% vs 11%; p < .01), a moderately lower local (86% vs 96%; p < .01), and a similar regional control (94% vs 99%; p = .07) compared to the negative planned neck dissection cohort. Extracapsular extension/carcinoma within soft tissue and lymphovascular invasion were adverse survival predictors for patients with positive planned neck dissection on univariate and multivariate analysis. CONCLUSION: Positive planned neck dissection is associated with lower survival, predominantly attributed to significantly increased DM rather than reduced locoregional control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".